无需降维，用逻辑规则构建可解释的混合数据聚类
PretopoMD: Pretopology-based Mixed Data Hierarchical Clustering
- 基于析取范式设计可定制逻辑规则，灵活控制聚类层级
- 直接从原始数据构建层次聚类，准确率与可解释性双提升
- 适合需要透明决策过程的医疗、金融等复杂数据场景
本文提出一种新型预拓扑混合数据层次聚类算法，无需降维即可处理异构数据。通过析取范式（Disjunctive Normal Form）构建可定制逻辑规则与可调超参数，实现用户定义的层次聚类结构，支持对异质数据集的定制化解决方案。基于层次树状图分析和对比聚类指标，该方法在不丢失数据完整性的前提下，精准且可解释地划分出簇，实证结果表明其在构建有意义聚类方面具有鲁棒性，并有效缓解聚类结果可解释性难题。本工作创新性地摆脱传统降维依赖，利用逻辑规则增强聚类形成与清晰度，为混合数据聚类研究带来显著进展。
原文摘要 · Abstract (English)
This article presents a novel pretopology-based algorithm designed to address the challenges of clustering mixed data without the need for dimensionality reduction. Leveraging Disjunctive Normal Form, our approach formulates customizable logical rules and adjustable hyperparameters that allow for user-defined hierarchical cluster construction and facilitate tailored solutions for heterogeneous datasets. Through hierarchical dendrogram analysis and comparative clustering metrics, our method demonstrates superior performance by accurately and interpretably delineating clusters directly from raw data, thus preserving data integrity. Empirical findings highlight the algorithm's robustness in constructing meaningful clusters and reveal its potential in overcoming issues related to clustered data explainability. The novelty of this work lies in its departure from traditional dimensionality reduction techniques and its innovative use of logical rules that enhance both cluster formation and clarity, thereby contributing a significant advancement to the discourse on clustering mixed data.
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